{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d209556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
      "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
      "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
      "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
      "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
      "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
      "\n",
      "     who  adult_male deck  embark_town alive  alone  \n",
      "0    man        True  NaN  Southampton    no  False  \n",
      "1  woman       False    C    Cherbourg   yes  False  \n",
      "2  woman       False  NaN  Southampton   yes   True  \n",
      "3  woman       False    C  Southampton   yes  False  \n",
      "4    man        True  NaN  Southampton    no   True  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n",
      "info: None\n",
      "describe:          survived      pclass         age       sibsp       parch        fare\n",
      "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
      "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
      "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
      "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
      "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
      "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
      "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
      "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# 加载 Titanic 数据集\n",
    "titanic = sns.load_dataset('titanic')\n",
    "\n",
    "# 查看数据前几行\n",
    "print(titanic.head())\n",
    "# 查看 Titanic 数据的基本信息\n",
    "print(\"info:\",titanic.info())\n",
    "print(\"describe:\",titanic.describe())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60b0f360",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "female    27.915709\n",
      "male      30.726645\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 按照性别分组，计算每组的平均年龄\n",
    "print(titanic.groupby('sex')['age'].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e931206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
      "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
      "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
      "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
      "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
      "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
      "\n",
      "     who  adult_male deck  embark_town alive  alone  \n",
      "0    man        True  NaN  Southampton    no  False  \n",
      "1  woman       False    C    Cherbourg   yes  False  \n",
      "2  woman       False  NaN  Southampton   yes   True  \n",
      "3  woman       False    C  Southampton   yes  False  \n",
      "4    man        True  NaN  Southampton    no   True  \n"
     ]
    }
   ],
   "source": [
    "# 删除含有缺失值的行\n",
    "titanic_clean = titanic.dropna()\n",
    "\n",
    "# 用平均年龄填充年龄中的缺失值\n",
    "titanic['age'] = titanic['age'].fillna(titanic['age'].mean())\n",
    "print(titanic.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd19f54",
   "metadata": {},
   "source": [
    "使用 Matplotlib 进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "259989eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制年龄和票价的折线图\n",
    "plt.plot(titanic['age'], titanic['fare'], 'bo')\n",
    "plt.title(\"Age vs Fare\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Fare\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b01be84f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制乘客年龄与票价的散点图\n",
    "plt.scatter(titanic['age'], titanic['fare'], color='green')\n",
    "plt.title(\"Age vs Fare\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Fare\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19735ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每个性别乘客的生存人数条形图\n",
    "survived_counts = titanic.groupby('sex')['survived'].sum()\n",
    "plt.bar(survived_counts.index, survived_counts.values, color=['blue', 'orange'])\n",
    "plt.title(\"Survival Count by Gender\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Survived Count\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1078a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制乘客年龄的直方图\n",
    "plt.hist(titanic['age'].dropna(), bins=20, color='purple', alpha=0.7)\n",
    "plt.title(\"Age Distribution of Titanic Passengers\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04fedee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制生存与否的比例饼图\n",
    "survival_rate = titanic['survived'].value_counts()\n",
    "plt.pie(survival_rate, labels=['Not Survived', 'Survived'], autopct='%1.1f%%', colors=['red', 'green'])\n",
    "plt.title(\"Survival Rate on Titanic\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2de5b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制并保存图表\n",
    "plt.scatter(titanic['age'], titanic['fare'], color='blue')\n",
    "plt.title(\"Age vs Fare\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Fare\")\n",
    "plt.grid(True)\n",
    "plt.savefig('age_vs_fare.png')  # 保存图表为 PNG 文件\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9374966d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mathjian",
   "language": "python",
   "name": "mathjian"
  },
  "language_info": {
   "codemirror_mode": {
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